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Record W1992795506 · doi:10.1001/jama.2009.1821

Eliminating “Waste” in Health Care

2009· article· en· W1992795506 on OpenAlexaboutno aff
Victor R. Fuchs

Bibliographic record

VenueJAMA · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHealth careWaste managementIntensive care medicineMedical emergencyEconomic growth

Abstract

fetched live from OpenAlex

PRESIDENT OBAMA IS THE MOST RECENT IN A LONG LINE of US presidents to seek reductions in health care spending through elimination of “waste.” However, the stakes this time are unusually high—the president has reported that eliminating waste is needed to fund two-thirds of the approximately $900 billion needed (over 10 years) for expanded health care coverage. To achieve this goal requires defining waste, identifying contexts in which it occurs, determining why it occurs, and implementing policies that prevent reoccurrence. Defining waste in medical care is not simple. Consider, for example, a patient who has experienced frequent, intermittent headaches for several weeks. Her physician thinks it is unlikely that the headaches are caused by a brain tumor or lesion (less than 1 chance in 10). A magnetic resonance imaging scan would provide more definite information. If the physician orders the scan, is that waste? What if the chances were 1 in 100 or 1 in 1000? What if the patient is so anxious about the headaches that she has difficulty with daily functions? Should that affect the definition of waste? As another example, consider 10 members of a college football team who are found to have a disease that has 2 possible interventions. Bed rest, fluids, and over-the-counter medications for relief of symptoms would result in recovery of all 10 patients in about 2 weeks; administration of a new, expensive drug would likely cure 7 patients within 2 or 3 days, send 1 patient to the hospital, and have no effect on the others. Is it wasteful to give the drug—or not to give it? These examples lead to considering 2 possible definitions of waste in medical care. Medical waste is defined as any intervention that has no possible benefit for the patient or in which the potential risk to the patient is greater than potential benefit. Economic waste is defined as any intervention for which the value of expected benefit is less than expected costs. The proportion of care deemed wasteful using the medical definition is much smaller than that deemed wasteful using the economic definition. Medical waste could occur only if the physician is misinformed, if the patient is misinformed and the physician succumbs to patient demands, or if the physician behaves unethically. Economic waste is much more common because of third-party payment. A conscientious clinician treating an insured patient would tend to recommend any intervention with a potential benefit greater than the potential risk. Two ubiquitous aspects of medical care make identification of waste particularly problematic. First, there is little certainty in medicine. Implicitly, if not explicitly, physicians are usually dealing with probabilities. Many interventions appear to have been wasteful in retrospect, but that is not the correct criterion; only prospective probability of success is relevant. The oft-heard promise “we will find out what works and what does not” scarcely does justice to the complexity of medical practice. Some interventions are undoubtedly useless, but those that might help some patients are much more common. Second, patients differ in unpredictable ways. The same drug given to patients with the same diagnosis often has different effects, ranging from rapid cure to serious adverse reaction. Any effort to reduce costs on a large scale requires consideration of economic waste. Where in medical practice is economic waste likely to be found? Almost everywhere. Some patients do not receive sufficient screening because of lack of insurance, inertia, or fear, but for the US population as a whole, the error is probably on the side of excess screening. On a per capita basis, patients in the United States receive almost 3 times as many magnetic resonance imaging scans as those in Canada. Are the benefits of extra scans enough to justify the extra cost? Repeated testing is another area with high potential for economic waste. There is usually little scientific foundation for the appropriate interval between tests and even less economic analysis of benefits and costs of alternative intervals. For a variety of reasons, including pressure from patients, physicians prescribe brand-name drugs when generic medications would be as effective or no drug at all would be best. An analogous situation may be the choice between a high-cost device or procedure and a less expensive alternative. For example, high-cost drug-eluting stents may be the better choice for some patients, but others would do just as well with less expensive bare-metal stents. Some patients are hospitalized for what might be wasteful reasons. For example, the patient’s insurance coverage might be better in hospital, compensation to the physician

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.480
GPT teacher head0.572
Teacher spread0.092 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2009
Admission routes1
Has abstractyes

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